Single-cell RNA_Seq data analysis in R using Seurat
6 weeks HDM data vs 2 weeks HDM data and dog allergen data
JEBUNNAHAR MISHU
Loading the libraries
library(BiocManager)
library(devtools)
## Loading required package: usethis
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## Attaching package: 'devtools'
## The following object is masked from 'package:BiocManager':
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## install
library(Seurat)
## Loading required package: SeuratObject
## Loading required package: sp
## 'SeuratObject' was built under R 4.4.0 but the current version is
## 4.4.1; it is recomended that you reinstall 'SeuratObject' as the ABI
## for R may have changed
##
## Attaching package: 'SeuratObject'
## The following objects are masked from 'package:base':
##
## intersect, t
library(SeuratObject)
library(remotes)
##
## Attaching package: 'remotes'
## The following objects are masked from 'package:devtools':
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## dev_package_deps, install_bioc, install_bitbucket, install_cran,
## install_deps, install_dev, install_git, install_github,
## install_gitlab, install_local, install_svn, install_url,
## install_version, update_packages
## The following object is masked from 'package:usethis':
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## git_credentials
library(targets)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
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library(ggplot2)
library(dplyr)
library(gridExtra)
##
## Attaching package: 'gridExtra'
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## combine
library(Matrix)
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## Attaching package: 'Matrix'
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## expand, pack, unpack
library(pheatmap)
library(harmony)
## Loading required package: Rcpp
Read the file
base_path <- "/Users/cbm737/Documents/development"
# Set the directory
setwd(base_path)
Dirs <- list.dirs(path = base_path, full.names = FALSE, recursive = FALSE)
Dirs
## [1] "DA2WK_filtered_feature_bc_matrix" "HDM2WK_filtered_feature_bc_matrix"
## [3] "HDM6WK_filtered_feature_bc_matrix"
#list.files(Dirs)
for (x in Dirs) {
name <- gsub('_filtered_feature_bc_matrix','', x)
# Construct file paths
mtx_path <- file.path(base_path, x, "matrix.mtx.gz")
features_path <- file.path(base_path, x, "features.tsv.gz")
barcodes_path <- file.path(base_path, x, "barcodes.tsv.gz")
# Read the data
DAHDM_count <- ReadMtx(mtx = mtx_path,
features = features_path,
cells = barcodes_path)
# create seurat object
assign(name, CreateSeuratObject(count = DAHDM_count))
}
Merge datasets
HDMDA_Obj <- merge(HDM6WK, y = c(HDM2WK, DA2WK), add.cell.ids = c("HDM6_6WK", "HDM2_2WK", "DA2_2WK"), project = "HDMvsDA")
#str(HDMDA_Obj)
#View(HDMDA_Obj@meta.data)
head(HDMDA_Obj@meta.data)
## orig.ident nCount_RNA nFeature_RNA
## HDM6_6WK_AAACCTGCAATTCCTT-1 SeuratProject 1859 962
## HDM6_6WK_AAACCTGCACATTTCT-1 SeuratProject 6871 2588
## HDM6_6WK_AAACCTGCATCCGGGT-1 SeuratProject 3226 1227
## HDM6_6WK_AAACCTGGTACAGTGG-1 SeuratProject 1637 842
## HDM6_6WK_AAACCTGGTATGCTTG-1 SeuratProject 20947 4600
## HDM6_6WK_AAACCTGGTCAGATAA-1 SeuratProject 1919 1185
HDMDA_Obj
## An object of class Seurat
## 33502 features across 754038 samples within 1 assay
## Active assay: RNA (33502 features, 0 variable features)
## 3 layers present: counts.1, counts.2, counts.3
# create the column
HDMDA_Obj$Sample <- rownames(HDMDA_Obj@meta.data)
# splite the column
HDMDA_Obj@meta.data <- separate(HDMDA_Obj@meta.data, col = 'Sample', into = c('Id', 'Time', 'Barcode'), sep = '_')
unique(HDMDA_Obj@meta.data$Id)
## [1] "HDM6" "HDM2" "DA2"
# Joinlayers
HDMDA_Obj <- JoinLayers(HDMDA_Obj)
Quality control
# percentage mitocondrial
HDMDA_Obj[["PercentMT"]] <- PercentageFeatureSet(HDMDA_Obj, pattern = "^mt-")
# Removing Tcr
HDMDA_Obj <- HDMDA_Obj[!grepl('^Tr[abdg][vjc]', rownames(HDMDA_Obj))] # mouse
HDMDA_Obj
## An object of class Seurat
## 33248 features across 754038 samples within 1 assay
## Active assay: RNA (33248 features, 0 variable features)
## 1 layer present: counts
head(HDMDA_Obj@meta.data)
## orig.ident nCount_RNA nFeature_RNA Id Time
## HDM6_6WK_AAACCTGCAATTCCTT-1 SeuratProject 1859 962 HDM6 6WK
## HDM6_6WK_AAACCTGCACATTTCT-1 SeuratProject 6871 2588 HDM6 6WK
## HDM6_6WK_AAACCTGCATCCGGGT-1 SeuratProject 3226 1227 HDM6 6WK
## HDM6_6WK_AAACCTGGTACAGTGG-1 SeuratProject 1637 842 HDM6 6WK
## HDM6_6WK_AAACCTGGTATGCTTG-1 SeuratProject 20947 4600 HDM6 6WK
## HDM6_6WK_AAACCTGGTCAGATAA-1 SeuratProject 1919 1185 HDM6 6WK
## Barcode PercentMT
## HDM6_6WK_AAACCTGCAATTCCTT-1 AAACCTGCAATTCCTT-1 2.850995
## HDM6_6WK_AAACCTGCACATTTCT-1 AAACCTGCACATTTCT-1 1.673701
## HDM6_6WK_AAACCTGCATCCGGGT-1 AAACCTGCATCCGGGT-1 4.463732
## HDM6_6WK_AAACCTGGTACAGTGG-1 AAACCTGGTACAGTGG-1 3.543067
## HDM6_6WK_AAACCTGGTATGCTTG-1 AAACCTGGTATGCTTG-1 1.446508
## HDM6_6WK_AAACCTGGTCAGATAA-1 AAACCTGGTCAGATAA-1 2.397082
# Visualize data before filtering as a violin plot
VlnPlot(HDMDA_Obj, features = c("nCount_RNA", "nFeature_RNA","PercentMT"), ncol = 3)
## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
## Warning: Removed 490493 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 490493 rows containing missing values or values outside the scale range
## (`geom_point()`).
# filtering
HDMDA_Obj_filtered <- subset(HDMDA_Obj, subset = nCount_RNA < 20000 & nFeature_RNA > 200 & PercentMT < 15)
HDMDA_Obj_filtered
## An object of class Seurat
## 33248 features across 22709 samples within 1 assay
## Active assay: RNA (33248 features, 0 variable features)
## 1 layer present: counts
# Visualize data before normalization as a violin plot
VlnPlot(HDMDA_Obj_filtered, features = c("nCount_RNA", "nFeature_RNA","PercentMT"), ncol = 3)
## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
VlnPlot(HDMDA_Obj_filtered, features = c("nCount_RNA", "nFeature_RNA","PercentMT"), ncol = 3, pt.size = 0)
## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
# Feature Scatter is used to visualize feature to feature relations
FeatureScatter(HDMDA_Obj_filtered, feature1 = "nCount_RNA", feature2 = "nFeature_RNA") +
geom_smooth(method = lm)
## `geom_smooth()` using formula = 'y ~ x'
Normalize the data, Find the variables features, Scale the data to
avoid technical noise (batch effect) or biological sources ( different
cell cycle), perform linear dimension reduction, Find neighbors,
clusters and Run UMAP to visualization
HDMDA_Obj_Norm <- NormalizeData(object = HDMDA_Obj_filtered) %>%
FindVariableFeatures() %>%
ScaleData() %>%
RunPCA() %>%
FindNeighbors(dims = 1:17) %>%
FindClusters(resolution = c(0.3, 0.5, 0.8)) %>%
RunUMAP(dims = 1:17)
## Normalizing layer: counts
## Finding variable features for layer counts
## Centering and scaling data matrix
## PC_ 1
## Positive: S100a10, Thy1, S100a11, AW112010, Crip1, Id2, S100a6, Lgals1, Cxcr6, Vim
## Maf, Tmsb10, Icos, S100a4, Rora, Tagln2, Emb, Ahnak, Rgs1, Il18r1
## Ramp1, Gapdh, Ifi27l2a, Hmgb2, Ly6a, Il7r, Ccr2, Lsp1, Ifngr1, Glrx
## Negative: H2-Aa, H2-Eb1, H2-Ab1, Cd74, Cd79a, H2-DMb2, Cd79b, Bank1, Ly6d, Fcmr
## Ms4a1, Ebf1, H2-Ob, Iglc3, Ly86, Mef2c, Iglc2, Cd83, Cd19, Blnk
## Apoe, Ifi30, Ciita, Ctsh, Fcrla, Ighd, H2-Oa, Igkc, Syk, Napsa
## PC_ 2
## Positive: Tcf7, Klf2, Tmsb10, Rflnb, Ccr7, Cd79a, Igfbp4, Cd79b, S1pr1, Igkc
## Iglc2, Nsg2, Hmgn1, Ighm, Gimap6, H2-Ob, Slamf6, Iglc3, Ms4a1, Sell
## Lef1, Ebf1, Actn1, H2-DMb2, Ly6d, 1110034G24Rik, Bank1, Fcmr, Cd19, Mef2c
## Negative: Il1rl1, Fgl2, Lgals1, Ahnak, CAAA01147332.1, Dleu2, Atp5md, Tle5, Serinc3, Arl5a
## Rgs1, 2410006H16Rik, Aopep, Il13, Vim, Atp5mpl, Ndufb1-ps, Gas5, Lgmn, Neb
## Fabp5, Rbpj, Lgals3, Il2ra, Atp5o, Micos10, Cd200r1, Adam8, Castor1, Capg
## PC_ 3
## Positive: Tle5, Il1rl1, Serinc3, Gm43305, Dleu2, Fgl2, 2410006H16Rik, 1810026B05Rik, Bank1, Cd79b
## Cd79a, Arl5a, Gas5, Fcmr, CAAA01147332.1, Plaat3, Neb, Tmem134, Ebf1, Ms4a1
## H2-DMb2, Castor1, Il13, Atp5md, Ly6d, Cd19, H2-Ob, Tut4, Atp5mpl, Il2ra
## Negative: Lyz2, Fcer1g, Tyrobp, Sirpa, Cd68, Mpeg1, Tgfbi, App, Cd63, Lrp1
## Fn1, Csf2rb, F10, Eps8, Cd9, Fcgr3, Cd300lf, Tnfaip2, Ctsk, Chil3
## Wfdc17, Cebpa, Alox5ap, Emilin2, Tgm2, Anpep, Gda, Rgl1, Clec4a3, Ccl6
## PC_ 4
## Positive: Tle5, Gas5, Atp5md, Atp5mpl, 2410006H16Rik, Resf1, 1810026B05Rik, Ndufb1-ps, Atp5o, Il7r
## AY036118, Cct6a, Micos10, Rbis, Dleu2, Plaat3, Aopep, Snhg8, Micos13, Oip5os1
## Tmem134, Otulinl, Oga, Tut4, Rab5if, Tomm6, Shld1, Ciao2b, Gm2682, Trerf1
## Negative: Birc5, Pclaf, Ccna2, Ube2c, Nusap1, Cdca3, Spc24, Cdca8, Stmn1, Ccnb2
## Cks1b, Cenpf, Tpx2, Top2a, Esco2, Rrm2, Ska1, Kif11, Aurkb, Cenpe
## Cenpm, Cdk1, Mki67, Clspn, Hist1h2ab, Cit, Ckap2l, Mxd3, Knl1, Rad51ap1
## PC_ 5
## Positive: Capg, S100a4, S100a11, S100a6, Vim, S100a10, Id2, Ramp1, Ucp2, Cd52
## Lgals1, Crip1, Cxcr6, Igfbp7, Rora, Aqp3, Glrx, Maf, Tagln2, Gpx1
## Fth1, Litaf, Ccr2, Lsp1, Ccr6, Pmm1, Socs2, Actg1, Rbpj, Tmem176b
## Negative: Tle5, Gas5, Atp5md, Atp5mpl, Tcf7, 2410006H16Rik, Resf1, Atp5o, 1810026B05Rik, Ndufb1-ps
## Satb1, Klf2, Gm2682, Cct6a, Klf3, Micos10, Plaat3, Ms4a4b, Rab5if, Otulinl
## Igfbp4, Rbis, AY036118, Oga, Snhg8, Micos13, Oip5os1, Ccr7, Shld1, Aopep
## Computing nearest neighbor graph
## Computing SNN
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 22709
## Number of edges: 758934
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9215
## Number of communities: 13
## Elapsed time: 2 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 22709
## Number of edges: 758934
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9010
## Number of communities: 19
## Elapsed time: 2 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 22709
## Number of edges: 758934
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8797
## Number of communities: 21
## Elapsed time: 2 seconds
## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session
## 16:30:06 UMAP embedding parameters a = 0.9922 b = 1.112
## 16:30:06 Read 22709 rows and found 17 numeric columns
## 16:30:06 Using Annoy for neighbor search, n_neighbors = 30
## 16:30:06 Building Annoy index with metric = cosine, n_trees = 50
## 0% 10 20 30 40 50 60 70 80 90 100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 16:30:08 Writing NN index file to temp file /var/folders/g4/2by5qwfx58b8zrlppt8n38300000gn/T//RtmpRVTgSd/file1bab75634c40
## 16:30:08 Searching Annoy index using 1 thread, search_k = 3000
## 16:30:11 Annoy recall = 100%
## 16:30:12 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 16:30:12 Initializing from normalized Laplacian + noise (using RSpectra)
## 16:30:13 Commencing optimization for 200 epochs, with 967326 positive edges
## 16:30:19 Optimization finished
str(HDMDA_Obj_Norm)
## Formal class 'Seurat' [package "SeuratObject"] with 13 slots
## ..@ assays :List of 1
## .. ..$ RNA:Formal class 'Assay5' [package "SeuratObject"] with 8 slots
## .. .. .. ..@ layers :List of 3
## .. .. .. .. ..$ counts :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
## .. .. .. .. .. .. ..@ i : int [1:38377842] 38 52 63 69 118 123 167 180 193 219 ...
## .. .. .. .. .. .. ..@ p : int [1:22710] 0 958 3543 4770 5611 6794 8671 10725 12629 14008 ...
## .. .. .. .. .. .. ..@ Dim : int [1:2] 33248 22709
## .. .. .. .. .. .. ..@ Dimnames:List of 2
## .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. ..@ x : num [1:38377842] 1 1 2 1 1 1 1 1 2 1 ...
## .. .. .. .. .. .. ..@ factors : list()
## .. .. .. .. ..$ data :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
## .. .. .. .. .. .. ..@ i : int [1:38377842] 38 52 63 69 118 123 167 180 193 219 ...
## .. .. .. .. .. .. ..@ p : int [1:22710] 0 958 3543 4770 5611 6794 8671 10725 12629 14008 ...
## .. .. .. .. .. .. ..@ Dim : int [1:2] 33248 22709
## .. .. .. .. .. .. ..@ Dimnames:List of 2
## .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. ..@ x : num [1:38377842] 1.86 1.86 2.47 1.86 1.86 ...
## .. .. .. .. .. .. ..@ factors : list()
## .. .. .. .. ..$ scale.data: num [1:2000, 1:22709] -0.0124 -0.0119 -0.0201 -0.0132 -0.197 ...
## .. .. .. ..@ cells :Formal class 'LogMap' [package "SeuratObject"] with 1 slot
## .. .. .. .. .. ..@ .Data: logi [1:22709, 1:3] TRUE TRUE TRUE TRUE TRUE TRUE ...
## .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. ..$ : chr [1:22709] "HDM6_6WK_AAACCTGCAATTCCTT-1" "HDM6_6WK_AAACCTGCACATTTCT-1" "HDM6_6WK_AAACCTGCATCCGGGT-1" "HDM6_6WK_AAACCTGGTACAGTGG-1" ...
## .. .. .. .. .. .. .. ..$ : chr [1:3] "counts" "data" "scale.data"
## .. .. .. .. .. ..$ dim : int [1:2] 22709 3
## .. .. .. .. .. ..$ dimnames:List of 2
## .. .. .. .. .. .. ..$ : chr [1:22709] "HDM6_6WK_AAACCTGCAATTCCTT-1" "HDM6_6WK_AAACCTGCACATTTCT-1" "HDM6_6WK_AAACCTGCATCCGGGT-1" "HDM6_6WK_AAACCTGGTACAGTGG-1" ...
## .. .. .. .. .. .. ..$ : chr [1:3] "counts" "data" "scale.data"
## .. .. .. ..@ features :Formal class 'LogMap' [package "SeuratObject"] with 1 slot
## .. .. .. .. .. ..@ .Data: logi [1:33248, 1:3] TRUE TRUE TRUE TRUE TRUE TRUE ...
## .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. ..$ : chr [1:33248] "Xkr4" "Gm1992" "Gm19938" "Gm37381" ...
## .. .. .. .. .. .. .. ..$ : chr [1:3] "counts" "data" "scale.data"
## .. .. .. .. .. ..$ dim : int [1:2] 33248 3
## .. .. .. .. .. ..$ dimnames:List of 2
## .. .. .. .. .. .. ..$ : chr [1:33248] "Xkr4" "Gm1992" "Gm19938" "Gm37381" ...
## .. .. .. .. .. .. ..$ : chr [1:3] "counts" "data" "scale.data"
## .. .. .. ..@ default : int 1
## .. .. .. ..@ assay.orig: chr(0)
## .. .. .. ..@ meta.data :'data.frame': 33248 obs. of 8 variables:
## .. .. .. .. ..$ vf_vst_counts_mean : num [1:33248] 0.0 0.0 0.0 4.4e-05 0.0 ...
## .. .. .. .. ..$ vf_vst_counts_variance : num [1:33248] 0.0 0.0 0.0 4.4e-05 0.0 ...
## .. .. .. .. ..$ vf_vst_counts_variance.expected : num [1:33248] 0.0 0.0 0.0 4.4e-05 0.0 ...
## .. .. .. .. ..$ vf_vst_counts_variance.standardized: num [1:33248] 0 0 0 1 0 ...
## .. .. .. .. ..$ vf_vst_counts_variable : logi [1:33248] FALSE FALSE FALSE FALSE FALSE TRUE ...
## .. .. .. .. ..$ vf_vst_counts_rank : int [1:33248] NA NA NA NA NA 1194 NA NA NA NA ...
## .. .. .. .. ..$ var.features : chr [1:33248] NA NA NA NA ...
## .. .. .. .. ..$ var.features.rank : int [1:33248] NA NA NA NA NA 1194 NA NA NA NA ...
## .. .. .. ..@ misc : list()
## .. .. .. ..@ key : chr "rna_"
## ..@ meta.data :'data.frame': 22709 obs. of 11 variables:
## .. ..$ orig.ident : chr [1:22709] "SeuratProject" "SeuratProject" "SeuratProject" "SeuratProject" ...
## .. ..$ nCount_RNA : num [1:22709] 1859 6871 3226 1637 1919 ...
## .. ..$ nFeature_RNA : int [1:22709] 962 2588 1227 842 1185 1881 2058 1909 1385 1474 ...
## .. ..$ Id : chr [1:22709] "HDM6" "HDM6" "HDM6" "HDM6" ...
## .. ..$ Time : chr [1:22709] "6WK" "6WK" "6WK" "6WK" ...
## .. ..$ Barcode : chr [1:22709] "AAACCTGCAATTCCTT-1" "AAACCTGCACATTTCT-1" "AAACCTGCATCCGGGT-1" "AAACCTGGTACAGTGG-1" ...
## .. ..$ PercentMT : num [1:22709] 2.85 1.67 4.46 3.54 2.4 ...
## .. ..$ RNA_snn_res.0.3: Factor w/ 13 levels "0","1","2","3",..: 5 5 9 1 4 4 4 5 5 9 ...
## .. ..$ RNA_snn_res.0.5: Factor w/ 19 levels "0","1","2","3",..: 13 3 12 14 13 13 5 3 3 12 ...
## .. ..$ RNA_snn_res.0.8: Factor w/ 21 levels "0","1","2","3",..: 16 3 14 15 16 13 5 3 3 14 ...
## .. ..$ seurat_clusters: Factor w/ 21 levels "0","1","2","3",..: 16 3 14 15 16 13 5 3 3 14 ...
## ..@ active.assay: chr "RNA"
## ..@ active.ident: Factor w/ 21 levels "0","1","2","3",..: 16 3 14 15 16 13 5 3 3 14 ...
## .. ..- attr(*, "names")= chr [1:22709] "HDM6_6WK_AAACCTGCAATTCCTT-1" "HDM6_6WK_AAACCTGCACATTTCT-1" "HDM6_6WK_AAACCTGCATCCGGGT-1" "HDM6_6WK_AAACCTGGTACAGTGG-1" ...
## ..@ graphs :List of 2
## .. ..$ RNA_nn :Formal class 'Graph' [package "SeuratObject"] with 7 slots
## .. .. .. ..@ assay.used: chr "RNA"
## .. .. .. ..@ i : int [1:454180] 0 995 1024 1599 3985 4150 4274 4475 5376 17394 ...
## .. .. .. ..@ p : int [1:22710] 0 10 41 67 79 84 102 134 148 165 ...
## .. .. .. ..@ Dim : int [1:2] 22709 22709
## .. .. .. ..@ Dimnames :List of 2
## .. .. .. .. ..$ : chr [1:22709] "HDM6_6WK_AAACCTGCAATTCCTT-1" "HDM6_6WK_AAACCTGCACATTTCT-1" "HDM6_6WK_AAACCTGCATCCGGGT-1" "HDM6_6WK_AAACCTGGTACAGTGG-1" ...
## .. .. .. .. ..$ : chr [1:22709] "HDM6_6WK_AAACCTGCAATTCCTT-1" "HDM6_6WK_AAACCTGCACATTTCT-1" "HDM6_6WK_AAACCTGCATCCGGGT-1" "HDM6_6WK_AAACCTGGTACAGTGG-1" ...
## .. .. .. ..@ x : num [1:454180] 1 1 1 1 1 1 1 1 1 1 ...
## .. .. .. ..@ factors : list()
## .. ..$ RNA_snn:Formal class 'Graph' [package "SeuratObject"] with 7 slots
## .. .. .. ..@ assay.used: chr "RNA"
## .. .. .. ..@ i : int [1:1540577] 0 508 605 715 995 1024 1084 1156 1553 1575 ...
## .. .. .. ..@ p : int [1:22710] 0 42 111 220 251 290 366 465 522 602 ...
## .. .. .. ..@ Dim : int [1:2] 22709 22709
## .. .. .. ..@ Dimnames :List of 2
## .. .. .. .. ..$ : chr [1:22709] "HDM6_6WK_AAACCTGCAATTCCTT-1" "HDM6_6WK_AAACCTGCACATTTCT-1" "HDM6_6WK_AAACCTGCATCCGGGT-1" "HDM6_6WK_AAACCTGGTACAGTGG-1" ...
## .. .. .. .. ..$ : chr [1:22709] "HDM6_6WK_AAACCTGCAATTCCTT-1" "HDM6_6WK_AAACCTGCACATTTCT-1" "HDM6_6WK_AAACCTGCATCCGGGT-1" "HDM6_6WK_AAACCTGGTACAGTGG-1" ...
## .. .. .. ..@ x : num [1:1540577] 1 0.1111 0.0811 0.1111 0.25 ...
## .. .. .. ..@ factors : list()
## ..@ neighbors : list()
## ..@ reductions :List of 2
## .. ..$ pca :Formal class 'DimReduc' [package "SeuratObject"] with 9 slots
## .. .. .. ..@ cell.embeddings : num [1:22709, 1:50] 0.885 -1.466 -31.677 0.699 -1.37 ...
## .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. ..$ : chr [1:22709] "HDM6_6WK_AAACCTGCAATTCCTT-1" "HDM6_6WK_AAACCTGCACATTTCT-1" "HDM6_6WK_AAACCTGCATCCGGGT-1" "HDM6_6WK_AAACCTGGTACAGTGG-1" ...
## .. .. .. .. .. ..$ : chr [1:50] "PC_1" "PC_2" "PC_3" "PC_4" ...
## .. .. .. ..@ feature.loadings : num [1:2000, 1:50] -0.0236 -0.1313 -0.0414 0.0053 -0.1367 ...
## .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. ..$ : chr [1:2000] "Chil3" "Cd74" "Lyz2" "Ccl5" ...
## .. .. .. .. .. ..$ : chr [1:50] "PC_1" "PC_2" "PC_3" "PC_4" ...
## .. .. .. ..@ feature.loadings.projected: num[0 , 0 ]
## .. .. .. ..@ assay.used : chr "RNA"
## .. .. .. ..@ global : logi FALSE
## .. .. .. ..@ stdev : num [1:50] 6.34 5.34 4.72 4.46 4 ...
## .. .. .. ..@ jackstraw :Formal class 'JackStrawData' [package "SeuratObject"] with 4 slots
## .. .. .. .. .. ..@ empirical.p.values : num[0 , 0 ]
## .. .. .. .. .. ..@ fake.reduction.scores : num[0 , 0 ]
## .. .. .. .. .. ..@ empirical.p.values.full: num[0 , 0 ]
## .. .. .. .. .. ..@ overall.p.values : num[0 , 0 ]
## .. .. .. ..@ misc :List of 1
## .. .. .. .. ..$ total.variance: num 958
## .. .. .. ..@ key : chr "PC_"
## .. ..$ umap:Formal class 'DimReduc' [package "SeuratObject"] with 9 slots
## .. .. .. ..@ cell.embeddings : num [1:22709, 1:2] -0.848 -4.571 -13.325 -0.866 -2.916 ...
## .. .. .. .. ..- attr(*, "scaled:center")= num [1:2] 0.118 1.03
## .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. ..$ : chr [1:22709] "HDM6_6WK_AAACCTGCAATTCCTT-1" "HDM6_6WK_AAACCTGCACATTTCT-1" "HDM6_6WK_AAACCTGCATCCGGGT-1" "HDM6_6WK_AAACCTGGTACAGTGG-1" ...
## .. .. .. .. .. ..$ : chr [1:2] "umap_1" "umap_2"
## .. .. .. ..@ feature.loadings : num[0 , 0 ]
## .. .. .. ..@ feature.loadings.projected: num[0 , 0 ]
## .. .. .. ..@ assay.used : chr "RNA"
## .. .. .. ..@ global : logi TRUE
## .. .. .. ..@ stdev : num(0)
## .. .. .. ..@ jackstraw :Formal class 'JackStrawData' [package "SeuratObject"] with 4 slots
## .. .. .. .. .. ..@ empirical.p.values : num[0 , 0 ]
## .. .. .. .. .. ..@ fake.reduction.scores : num[0 , 0 ]
## .. .. .. .. .. ..@ empirical.p.values.full: num[0 , 0 ]
## .. .. .. .. .. ..@ overall.p.values : num[0 , 0 ]
## .. .. .. ..@ misc : list()
## .. .. .. ..@ key : chr "umap_"
## ..@ images : list()
## ..@ project.name: chr "HDMvsDA"
## ..@ misc : list()
## ..@ version :Classes 'package_version', 'numeric_version' hidden list of 1
## .. ..$ : int [1:3] 5 0 2
## ..@ commands :List of 7
## .. ..$ NormalizeData.RNA :Formal class 'SeuratCommand' [package "SeuratObject"] with 5 slots
## .. .. .. ..@ name : chr "NormalizeData.RNA"
## .. .. .. ..@ time.stamp : POSIXct[1:1], format: "2024-08-02 16:29:42"
## .. .. .. ..@ assay.used : chr "RNA"
## .. .. .. ..@ call.string: chr "NormalizeData(object = HDMDA_Obj_filtered)"
## .. .. .. ..@ params :List of 5
## .. .. .. .. ..$ assay : chr "RNA"
## .. .. .. .. ..$ normalization.method: chr "LogNormalize"
## .. .. .. .. ..$ scale.factor : num 10000
## .. .. .. .. ..$ margin : num 1
## .. .. .. .. ..$ verbose : logi TRUE
## .. ..$ FindVariableFeatures.RNA:Formal class 'SeuratCommand' [package "SeuratObject"] with 5 slots
## .. .. .. ..@ name : chr "FindVariableFeatures.RNA"
## .. .. .. ..@ time.stamp : POSIXct[1:1], format: "2024-08-02 16:29:44"
## .. .. .. ..@ assay.used : chr "RNA"
## .. .. .. ..@ call.string: chr "FindVariableFeatures(.)"
## .. .. .. ..@ params :List of 12
## .. .. .. .. ..$ assay : chr "RNA"
## .. .. .. .. ..$ selection.method : chr "vst"
## .. .. .. .. ..$ loess.span : num 0.3
## .. .. .. .. ..$ clip.max : chr "auto"
## .. .. .. .. ..$ mean.function :function (mat, display_progress)
## .. .. .. .. ..$ dispersion.function:function (mat, display_progress)
## .. .. .. .. ..$ num.bin : num 20
## .. .. .. .. ..$ binning.method : chr "equal_width"
## .. .. .. .. ..$ nfeatures : num 2000
## .. .. .. .. ..$ mean.cutoff : num [1:2] 0.1 8
## .. .. .. .. ..$ dispersion.cutoff : num [1:2] 1 Inf
## .. .. .. .. ..$ verbose : logi TRUE
## .. ..$ ScaleData.RNA :Formal class 'SeuratCommand' [package "SeuratObject"] with 5 slots
## .. .. .. ..@ name : chr "ScaleData.RNA"
## .. .. .. ..@ time.stamp : POSIXct[1:1], format: "2024-08-02 16:29:44"
## .. .. .. ..@ assay.used : chr "RNA"
## .. .. .. ..@ call.string: chr "ScaleData(.)"
## .. .. .. ..@ params :List of 9
## .. .. .. .. ..$ assay : chr "RNA"
## .. .. .. .. ..$ model.use : chr "linear"
## .. .. .. .. ..$ use.umi : logi FALSE
## .. .. .. .. ..$ do.scale : logi TRUE
## .. .. .. .. ..$ do.center : logi TRUE
## .. .. .. .. ..$ scale.max : num 10
## .. .. .. .. ..$ block.size : num 1000
## .. .. .. .. ..$ min.cells.to.block: num 3000
## .. .. .. .. ..$ verbose : logi TRUE
## .. ..$ RunPCA.RNA :Formal class 'SeuratCommand' [package "SeuratObject"] with 5 slots
## .. .. .. ..@ name : chr "RunPCA.RNA"
## .. .. .. ..@ time.stamp : POSIXct[1:1], format: "2024-08-02 16:29:56"
## .. .. .. ..@ assay.used : chr "RNA"
## .. .. .. ..@ call.string: chr "RunPCA(.)"
## .. .. .. ..@ params :List of 10
## .. .. .. .. ..$ assay : chr "RNA"
## .. .. .. .. ..$ npcs : num 50
## .. .. .. .. ..$ rev.pca : logi FALSE
## .. .. .. .. ..$ weight.by.var : logi TRUE
## .. .. .. .. ..$ verbose : logi TRUE
## .. .. .. .. ..$ ndims.print : int [1:5] 1 2 3 4 5
## .. .. .. .. ..$ nfeatures.print: num 30
## .. .. .. .. ..$ reduction.name : chr "pca"
## .. .. .. .. ..$ reduction.key : chr "PC_"
## .. .. .. .. ..$ seed.use : num 42
## .. ..$ FindNeighbors.RNA.pca :Formal class 'SeuratCommand' [package "SeuratObject"] with 5 slots
## .. .. .. ..@ name : chr "FindNeighbors.RNA.pca"
## .. .. .. ..@ time.stamp : POSIXct[1:1], format: "2024-08-02 16:29:58"
## .. .. .. ..@ assay.used : chr "RNA"
## .. .. .. ..@ call.string: chr "FindNeighbors(., dims = 1:17)"
## .. .. .. ..@ params :List of 16
## .. .. .. .. ..$ reduction : chr "pca"
## .. .. .. .. ..$ dims : int [1:17] 1 2 3 4 5 6 7 8 9 10 ...
## .. .. .. .. ..$ assay : chr "RNA"
## .. .. .. .. ..$ k.param : num 20
## .. .. .. .. ..$ return.neighbor: logi FALSE
## .. .. .. .. ..$ compute.SNN : logi TRUE
## .. .. .. .. ..$ prune.SNN : num 0.0667
## .. .. .. .. ..$ nn.method : chr "annoy"
## .. .. .. .. ..$ n.trees : num 50
## .. .. .. .. ..$ annoy.metric : chr "euclidean"
## .. .. .. .. ..$ nn.eps : num 0
## .. .. .. .. ..$ verbose : logi TRUE
## .. .. .. .. ..$ do.plot : logi FALSE
## .. .. .. .. ..$ graph.name : chr [1:2] "RNA_nn" "RNA_snn"
## .. .. .. .. ..$ l2.norm : logi FALSE
## .. .. .. .. ..$ cache.index : logi FALSE
## .. ..$ FindClusters :Formal class 'SeuratCommand' [package "SeuratObject"] with 5 slots
## .. .. .. ..@ name : chr "FindClusters"
## .. .. .. ..@ time.stamp : POSIXct[1:1], format: "2024-08-02 16:30:06"
## .. .. .. ..@ assay.used : chr "RNA"
## .. .. .. ..@ call.string: chr "FindClusters(., resolution = c(0.3, 0.5, 0.8))"
## .. .. .. ..@ params :List of 11
## .. .. .. .. ..$ graph.name : chr "RNA_snn"
## .. .. .. .. ..$ cluster.name : chr [1:3] "RNA_snn_res.0.3" "RNA_snn_res.0.5" "RNA_snn_res.0.8"
## .. .. .. .. ..$ modularity.fxn : num 1
## .. .. .. .. ..$ resolution : num [1:3] 0.3 0.5 0.8
## .. .. .. .. ..$ method : chr "matrix"
## .. .. .. .. ..$ algorithm : num 1
## .. .. .. .. ..$ n.start : num 10
## .. .. .. .. ..$ n.iter : num 10
## .. .. .. .. ..$ random.seed : num 0
## .. .. .. .. ..$ group.singletons: logi TRUE
## .. .. .. .. ..$ verbose : logi TRUE
## .. ..$ RunUMAP.RNA.pca :Formal class 'SeuratCommand' [package "SeuratObject"] with 5 slots
## .. .. .. ..@ name : chr "RunUMAP.RNA.pca"
## .. .. .. ..@ time.stamp : POSIXct[1:1], format: "2024-08-02 16:30:19"
## .. .. .. ..@ assay.used : chr "RNA"
## .. .. .. ..@ call.string: chr "RunUMAP(., dims = 1:17)"
## .. .. .. ..@ params :List of 25
## .. .. .. .. ..$ dims : int [1:17] 1 2 3 4 5 6 7 8 9 10 ...
## .. .. .. .. ..$ reduction : chr "pca"
## .. .. .. .. ..$ assay : chr "RNA"
## .. .. .. .. ..$ slot : chr "data"
## .. .. .. .. ..$ umap.method : chr "uwot"
## .. .. .. .. ..$ return.model : logi FALSE
## .. .. .. .. ..$ n.neighbors : int 30
## .. .. .. .. ..$ n.components : int 2
## .. .. .. .. ..$ metric : chr "cosine"
## .. .. .. .. ..$ learning.rate : num 1
## .. .. .. .. ..$ min.dist : num 0.3
## .. .. .. .. ..$ spread : num 1
## .. .. .. .. ..$ set.op.mix.ratio : num 1
## .. .. .. .. ..$ local.connectivity : int 1
## .. .. .. .. ..$ repulsion.strength : num 1
## .. .. .. .. ..$ negative.sample.rate: int 5
## .. .. .. .. ..$ uwot.sgd : logi FALSE
## .. .. .. .. ..$ seed.use : int 42
## .. .. .. .. ..$ angular.rp.forest : logi FALSE
## .. .. .. .. ..$ densmap : logi FALSE
## .. .. .. .. ..$ dens.lambda : num 2
## .. .. .. .. ..$ dens.frac : num 0.3
## .. .. .. .. ..$ dens.var.shift : num 0.1
## .. .. .. .. ..$ verbose : logi TRUE
## .. .. .. .. ..$ reduction.name : chr "umap"
## ..@ tools : list()
head(HDMDA_Obj_Norm@meta.data)
## orig.ident nCount_RNA nFeature_RNA Id Time
## HDM6_6WK_AAACCTGCAATTCCTT-1 SeuratProject 1859 962 HDM6 6WK
## HDM6_6WK_AAACCTGCACATTTCT-1 SeuratProject 6871 2588 HDM6 6WK
## HDM6_6WK_AAACCTGCATCCGGGT-1 SeuratProject 3226 1227 HDM6 6WK
## HDM6_6WK_AAACCTGGTACAGTGG-1 SeuratProject 1637 842 HDM6 6WK
## HDM6_6WK_AAACCTGGTCAGATAA-1 SeuratProject 1919 1185 HDM6 6WK
## HDM6_6WK_AAACCTGGTCCAGTAT-1 SeuratProject 4150 1881 HDM6 6WK
## Barcode PercentMT RNA_snn_res.0.3
## HDM6_6WK_AAACCTGCAATTCCTT-1 AAACCTGCAATTCCTT-1 2.850995 4
## HDM6_6WK_AAACCTGCACATTTCT-1 AAACCTGCACATTTCT-1 1.673701 4
## HDM6_6WK_AAACCTGCATCCGGGT-1 AAACCTGCATCCGGGT-1 4.463732 8
## HDM6_6WK_AAACCTGGTACAGTGG-1 AAACCTGGTACAGTGG-1 3.543067 0
## HDM6_6WK_AAACCTGGTCAGATAA-1 AAACCTGGTCAGATAA-1 2.397082 3
## HDM6_6WK_AAACCTGGTCCAGTAT-1 AAACCTGGTCCAGTAT-1 2.024096 3
## RNA_snn_res.0.5 RNA_snn_res.0.8 seurat_clusters
## HDM6_6WK_AAACCTGCAATTCCTT-1 12 15 15
## HDM6_6WK_AAACCTGCACATTTCT-1 2 2 2
## HDM6_6WK_AAACCTGCATCCGGGT-1 11 13 13
## HDM6_6WK_AAACCTGGTACAGTGG-1 13 14 14
## HDM6_6WK_AAACCTGGTCAGATAA-1 12 15 15
## HDM6_6WK_AAACCTGGTCCAGTAT-1 12 12 12
ElbowPlot(HDMDA_Obj_Norm)
PCHeatmap(HDMDA_Obj_Norm, dim = 1:6, cells = 500, balanced =TRUE, ncol = 3)
DimPlot(HDMDA_Obj_Norm, reduction = "umap", group.by = "RNA_snn_res.0.5", label = TRUE, label.box = TRUE)+ labs(title = "Without Integration")
DimPlot(HDMDA_Obj_Norm, reduction = "umap", group.by = "RNA_snn_res.0.5", split.by = "Id", label = TRUE, label.box = TRUE)+ labs(title = "Without Integration")
Normalize the data, Find the variables features, Scale the data to
avoid technical noise (batch effect) or biological sources ( different
cell cycle), Perform linear dimension reduction, Integration using
Harmony to avoid technical noise (batch effect), Find neighbors,
clusters and Run UMAP to visualization
HDMDA_Obj_Harm <- NormalizeData(object = HDMDA_Obj_filtered) %>%
FindVariableFeatures() %>%
ScaleData() %>%
RunPCA() %>%
RunHarmony(group.by.vars = "Id") %>%
FindNeighbors(reduction = "harmony", dims = 1:17) %>%
FindClusters(resolution = 0.5) %>%
RunUMAP(reduction = "harmony", dims = 1:17)
## Normalizing layer: counts
## Finding variable features for layer counts
## Centering and scaling data matrix
## PC_ 1
## Positive: S100a10, Thy1, S100a11, AW112010, Crip1, Id2, S100a6, Lgals1, Cxcr6, Vim
## Maf, Tmsb10, Icos, S100a4, Rora, Tagln2, Emb, Ahnak, Rgs1, Il18r1
## Ramp1, Gapdh, Ifi27l2a, Hmgb2, Ly6a, Il7r, Ccr2, Lsp1, Ifngr1, Glrx
## Negative: H2-Aa, H2-Eb1, H2-Ab1, Cd74, Cd79a, H2-DMb2, Cd79b, Bank1, Ly6d, Fcmr
## Ms4a1, Ebf1, H2-Ob, Iglc3, Ly86, Mef2c, Iglc2, Cd83, Cd19, Blnk
## Apoe, Ifi30, Ciita, Ctsh, Fcrla, Ighd, H2-Oa, Igkc, Syk, Napsa
## PC_ 2
## Positive: Tcf7, Klf2, Tmsb10, Rflnb, Ccr7, Cd79a, Igfbp4, Cd79b, S1pr1, Igkc
## Iglc2, Nsg2, Hmgn1, Ighm, Gimap6, H2-Ob, Slamf6, Iglc3, Ms4a1, Sell
## Lef1, Ebf1, Actn1, H2-DMb2, Ly6d, 1110034G24Rik, Bank1, Fcmr, Cd19, Mef2c
## Negative: Il1rl1, Fgl2, Lgals1, Ahnak, CAAA01147332.1, Dleu2, Atp5md, Tle5, Serinc3, Arl5a
## Rgs1, 2410006H16Rik, Aopep, Il13, Vim, Atp5mpl, Ndufb1-ps, Gas5, Lgmn, Neb
## Fabp5, Rbpj, Lgals3, Il2ra, Atp5o, Micos10, Cd200r1, Adam8, Castor1, Capg
## PC_ 3
## Positive: Tle5, Il1rl1, Serinc3, Gm43305, Dleu2, Fgl2, 2410006H16Rik, 1810026B05Rik, Bank1, Cd79b
## Cd79a, Arl5a, Gas5, Fcmr, CAAA01147332.1, Plaat3, Neb, Tmem134, Ebf1, Ms4a1
## H2-DMb2, Castor1, Il13, Atp5md, Ly6d, Cd19, H2-Ob, Tut4, Atp5mpl, Il2ra
## Negative: Lyz2, Fcer1g, Tyrobp, Sirpa, Cd68, Mpeg1, Tgfbi, App, Cd63, Lrp1
## Fn1, Csf2rb, F10, Eps8, Cd9, Fcgr3, Cd300lf, Tnfaip2, Ctsk, Chil3
## Wfdc17, Cebpa, Alox5ap, Emilin2, Tgm2, Anpep, Gda, Rgl1, Clec4a3, Ccl6
## PC_ 4
## Positive: Tle5, Gas5, Atp5md, Atp5mpl, 2410006H16Rik, Resf1, 1810026B05Rik, Ndufb1-ps, Atp5o, Il7r
## AY036118, Cct6a, Micos10, Rbis, Dleu2, Plaat3, Aopep, Snhg8, Micos13, Oip5os1
## Tmem134, Otulinl, Oga, Tut4, Rab5if, Tomm6, Shld1, Ciao2b, Gm2682, Trerf1
## Negative: Birc5, Pclaf, Ccna2, Ube2c, Nusap1, Cdca3, Spc24, Cdca8, Stmn1, Ccnb2
## Cks1b, Cenpf, Tpx2, Top2a, Esco2, Rrm2, Ska1, Kif11, Aurkb, Cenpe
## Cenpm, Cdk1, Mki67, Clspn, Hist1h2ab, Cit, Ckap2l, Mxd3, Knl1, Rad51ap1
## PC_ 5
## Positive: Capg, S100a4, S100a11, S100a6, Vim, S100a10, Id2, Ramp1, Ucp2, Cd52
## Lgals1, Crip1, Cxcr6, Igfbp7, Rora, Aqp3, Glrx, Maf, Tagln2, Gpx1
## Fth1, Litaf, Ccr2, Lsp1, Ccr6, Pmm1, Socs2, Actg1, Rbpj, Tmem176b
## Negative: Tle5, Gas5, Atp5md, Atp5mpl, Tcf7, 2410006H16Rik, Resf1, Atp5o, 1810026B05Rik, Ndufb1-ps
## Satb1, Klf2, Gm2682, Cct6a, Klf3, Micos10, Plaat3, Ms4a4b, Rab5if, Otulinl
## Igfbp4, Rbis, AY036118, Oga, Snhg8, Micos13, Oip5os1, Ccr7, Shld1, Aopep
## Transposing data matrix
## Initializing state using k-means centroids initialization
## Harmony 1/10
## Harmony 2/10
## Harmony converged after 2 iterations
## Computing nearest neighbor graph
## Computing SNN
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 22709
## Number of edges: 743589
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8794
## Number of communities: 16
## Elapsed time: 2 seconds
## 16:30:51 UMAP embedding parameters a = 0.9922 b = 1.112
## 16:30:51 Read 22709 rows and found 17 numeric columns
## 16:30:51 Using Annoy for neighbor search, n_neighbors = 30
## 16:30:51 Building Annoy index with metric = cosine, n_trees = 50
## 0% 10 20 30 40 50 60 70 80 90 100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 16:30:51 Writing NN index file to temp file /var/folders/g4/2by5qwfx58b8zrlppt8n38300000gn/T//RtmpRVTgSd/file1bab12d98dec
## 16:30:51 Searching Annoy index using 1 thread, search_k = 3000
## 16:30:55 Annoy recall = 100%
## 16:30:56 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 16:30:56 Initializing from normalized Laplacian + noise (using RSpectra)
## 16:30:57 Commencing optimization for 200 epochs, with 983656 positive edges
## 16:31:03 Optimization finished
str(HDMDA_Obj_Harm)
## Formal class 'Seurat' [package "SeuratObject"] with 13 slots
## ..@ assays :List of 1
## .. ..$ RNA:Formal class 'Assay5' [package "SeuratObject"] with 8 slots
## .. .. .. ..@ layers :List of 3
## .. .. .. .. ..$ counts :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
## .. .. .. .. .. .. ..@ i : int [1:38377842] 38 52 63 69 118 123 167 180 193 219 ...
## .. .. .. .. .. .. ..@ p : int [1:22710] 0 958 3543 4770 5611 6794 8671 10725 12629 14008 ...
## .. .. .. .. .. .. ..@ Dim : int [1:2] 33248 22709
## .. .. .. .. .. .. ..@ Dimnames:List of 2
## .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. ..@ x : num [1:38377842] 1 1 2 1 1 1 1 1 2 1 ...
## .. .. .. .. .. .. ..@ factors : list()
## .. .. .. .. ..$ data :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
## .. .. .. .. .. .. ..@ i : int [1:38377842] 38 52 63 69 118 123 167 180 193 219 ...
## .. .. .. .. .. .. ..@ p : int [1:22710] 0 958 3543 4770 5611 6794 8671 10725 12629 14008 ...
## .. .. .. .. .. .. ..@ Dim : int [1:2] 33248 22709
## .. .. .. .. .. .. ..@ Dimnames:List of 2
## .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. .. ..@ x : num [1:38377842] 1.86 1.86 2.47 1.86 1.86 ...
## .. .. .. .. .. .. ..@ factors : list()
## .. .. .. .. ..$ scale.data: num [1:2000, 1:22709] -0.0124 -0.0119 -0.0201 -0.0132 -0.197 ...
## .. .. .. ..@ cells :Formal class 'LogMap' [package "SeuratObject"] with 1 slot
## .. .. .. .. .. ..@ .Data: logi [1:22709, 1:3] TRUE TRUE TRUE TRUE TRUE TRUE ...
## .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. ..$ : chr [1:22709] "HDM6_6WK_AAACCTGCAATTCCTT-1" "HDM6_6WK_AAACCTGCACATTTCT-1" "HDM6_6WK_AAACCTGCATCCGGGT-1" "HDM6_6WK_AAACCTGGTACAGTGG-1" ...
## .. .. .. .. .. .. .. ..$ : chr [1:3] "counts" "data" "scale.data"
## .. .. .. .. .. ..$ dim : int [1:2] 22709 3
## .. .. .. .. .. ..$ dimnames:List of 2
## .. .. .. .. .. .. ..$ : chr [1:22709] "HDM6_6WK_AAACCTGCAATTCCTT-1" "HDM6_6WK_AAACCTGCACATTTCT-1" "HDM6_6WK_AAACCTGCATCCGGGT-1" "HDM6_6WK_AAACCTGGTACAGTGG-1" ...
## .. .. .. .. .. .. ..$ : chr [1:3] "counts" "data" "scale.data"
## .. .. .. ..@ features :Formal class 'LogMap' [package "SeuratObject"] with 1 slot
## .. .. .. .. .. ..@ .Data: logi [1:33248, 1:3] TRUE TRUE TRUE TRUE TRUE TRUE ...
## .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. .. .. ..$ : chr [1:33248] "Xkr4" "Gm1992" "Gm19938" "Gm37381" ...
## .. .. .. .. .. .. .. ..$ : chr [1:3] "counts" "data" "scale.data"
## .. .. .. .. .. ..$ dim : int [1:2] 33248 3
## .. .. .. .. .. ..$ dimnames:List of 2
## .. .. .. .. .. .. ..$ : chr [1:33248] "Xkr4" "Gm1992" "Gm19938" "Gm37381" ...
## .. .. .. .. .. .. ..$ : chr [1:3] "counts" "data" "scale.data"
## .. .. .. ..@ default : int 1
## .. .. .. ..@ assay.orig: chr(0)
## .. .. .. ..@ meta.data :'data.frame': 33248 obs. of 8 variables:
## .. .. .. .. ..$ vf_vst_counts_mean : num [1:33248] 0.0 0.0 0.0 4.4e-05 0.0 ...
## .. .. .. .. ..$ vf_vst_counts_variance : num [1:33248] 0.0 0.0 0.0 4.4e-05 0.0 ...
## .. .. .. .. ..$ vf_vst_counts_variance.expected : num [1:33248] 0.0 0.0 0.0 4.4e-05 0.0 ...
## .. .. .. .. ..$ vf_vst_counts_variance.standardized: num [1:33248] 0 0 0 1 0 ...
## .. .. .. .. ..$ vf_vst_counts_variable : logi [1:33248] FALSE FALSE FALSE FALSE FALSE TRUE ...
## .. .. .. .. ..$ vf_vst_counts_rank : int [1:33248] NA NA NA NA NA 1194 NA NA NA NA ...
## .. .. .. .. ..$ var.features : chr [1:33248] NA NA NA NA ...
## .. .. .. .. ..$ var.features.rank : int [1:33248] NA NA NA NA NA 1194 NA NA NA NA ...
## .. .. .. ..@ misc : list()
## .. .. .. ..@ key : chr "rna_"
## ..@ meta.data :'data.frame': 22709 obs. of 9 variables:
## .. ..$ orig.ident : chr [1:22709] "SeuratProject" "SeuratProject" "SeuratProject" "SeuratProject" ...
## .. ..$ nCount_RNA : num [1:22709] 1859 6871 3226 1637 1919 ...
## .. ..$ nFeature_RNA : int [1:22709] 962 2588 1227 842 1185 1881 2058 1909 1385 1474 ...
## .. ..$ Id : chr [1:22709] "HDM6" "HDM6" "HDM6" "HDM6" ...
## .. ..$ Time : chr [1:22709] "6WK" "6WK" "6WK" "6WK" ...
## .. ..$ Barcode : chr [1:22709] "AAACCTGCAATTCCTT-1" "AAACCTGCACATTTCT-1" "AAACCTGCATCCGGGT-1" "AAACCTGGTACAGTGG-1" ...
## .. ..$ PercentMT : num [1:22709] 2.85 1.67 4.46 3.54 2.4 ...
## .. ..$ RNA_snn_res.0.5: Factor w/ 16 levels "0","1","2","3",..: 6 1 11 5 6 7 2 4 1 11 ...
## .. ..$ seurat_clusters: Factor w/ 16 levels "0","1","2","3",..: 6 1 11 5 6 7 2 4 1 11 ...
## ..@ active.assay: chr "RNA"
## ..@ active.ident: Factor w/ 16 levels "0","1","2","3",..: 6 1 11 5 6 7 2 4 1 11 ...
## .. ..- attr(*, "names")= chr [1:22709] "HDM6_6WK_AAACCTGCAATTCCTT-1" "HDM6_6WK_AAACCTGCACATTTCT-1" "HDM6_6WK_AAACCTGCATCCGGGT-1" "HDM6_6WK_AAACCTGGTACAGTGG-1" ...
## ..@ graphs :List of 2
## .. ..$ RNA_nn :Formal class 'Graph' [package "SeuratObject"] with 7 slots
## .. .. .. ..@ assay.used: chr "RNA"
## .. .. .. ..@ i : int [1:454180] 0 995 1599 3985 8528 9237 9472 11547 11573 14618 ...
## .. .. .. ..@ p : int [1:22710] 0 12 42 67 71 73 91 117 141 152 ...
## .. .. .. ..@ Dim : int [1:2] 22709 22709
## .. .. .. ..@ Dimnames :List of 2
## .. .. .. .. ..$ : chr [1:22709] "HDM6_6WK_AAACCTGCAATTCCTT-1" "HDM6_6WK_AAACCTGCACATTTCT-1" "HDM6_6WK_AAACCTGCATCCGGGT-1" "HDM6_6WK_AAACCTGGTACAGTGG-1" ...
## .. .. .. .. ..$ : chr [1:22709] "HDM6_6WK_AAACCTGCAATTCCTT-1" "HDM6_6WK_AAACCTGCACATTTCT-1" "HDM6_6WK_AAACCTGCATCCGGGT-1" "HDM6_6WK_AAACCTGGTACAGTGG-1" ...
## .. .. .. ..@ x : num [1:454180] 1 1 1 1 1 1 1 1 1 1 ...
## .. .. .. ..@ factors : list()
## .. ..$ RNA_snn:Formal class 'Graph' [package "SeuratObject"] with 7 slots
## .. .. .. ..@ assay.used: chr "RNA"
## .. .. .. ..@ i : int [1:1509887] 0 995 1024 1156 1553 2310 3985 4234 5012 7282 ...
## .. .. .. ..@ p : int [1:22710] 0 40 106 192 235 291 348 432 494 595 ...
## .. .. .. ..@ Dim : int [1:2] 22709 22709
## .. .. .. ..@ Dimnames :List of 2
## .. .. .. .. ..$ : chr [1:22709] "HDM6_6WK_AAACCTGCAATTCCTT-1" "HDM6_6WK_AAACCTGCACATTTCT-1" "HDM6_6WK_AAACCTGCATCCGGGT-1" "HDM6_6WK_AAACCTGGTACAGTGG-1" ...
## .. .. .. .. ..$ : chr [1:22709] "HDM6_6WK_AAACCTGCAATTCCTT-1" "HDM6_6WK_AAACCTGCACATTTCT-1" "HDM6_6WK_AAACCTGCATCCGGGT-1" "HDM6_6WK_AAACCTGGTACAGTGG-1" ...
## .. .. .. ..@ x : num [1:1509887] 1 0.1429 0.1429 0.0811 0.1429 ...
## .. .. .. ..@ factors : list()
## ..@ neighbors : list()
## ..@ reductions :List of 3
## .. ..$ pca :Formal class 'DimReduc' [package "SeuratObject"] with 9 slots
## .. .. .. ..@ cell.embeddings : num [1:22709, 1:50] 0.885 -1.466 -31.677 0.699 -1.37 ...
## .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. ..$ : chr [1:22709] "HDM6_6WK_AAACCTGCAATTCCTT-1" "HDM6_6WK_AAACCTGCACATTTCT-1" "HDM6_6WK_AAACCTGCATCCGGGT-1" "HDM6_6WK_AAACCTGGTACAGTGG-1" ...
## .. .. .. .. .. ..$ : chr [1:50] "PC_1" "PC_2" "PC_3" "PC_4" ...
## .. .. .. ..@ feature.loadings : num [1:2000, 1:50] -0.0236 -0.1313 -0.0414 0.0053 -0.1367 ...
## .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. ..$ : chr [1:2000] "Chil3" "Cd74" "Lyz2" "Ccl5" ...
## .. .. .. .. .. ..$ : chr [1:50] "PC_1" "PC_2" "PC_3" "PC_4" ...
## .. .. .. ..@ feature.loadings.projected: num[0 , 0 ]
## .. .. .. ..@ assay.used : chr "RNA"
## .. .. .. ..@ global : logi FALSE
## .. .. .. ..@ stdev : num [1:50] 6.34 5.34 4.72 4.46 4 ...
## .. .. .. ..@ jackstraw :Formal class 'JackStrawData' [package "SeuratObject"] with 4 slots
## .. .. .. .. .. ..@ empirical.p.values : num[0 , 0 ]
## .. .. .. .. .. ..@ fake.reduction.scores : num[0 , 0 ]
## .. .. .. .. .. ..@ empirical.p.values.full: num[0 , 0 ]
## .. .. .. .. .. ..@ overall.p.values : num[0 , 0 ]
## .. .. .. ..@ misc :List of 1
## .. .. .. .. ..$ total.variance: num 958
## .. .. .. ..@ key : chr "PC_"
## .. ..$ harmony:Formal class 'DimReduc' [package "SeuratObject"] with 9 slots
## .. .. .. ..@ cell.embeddings : num [1:22709, 1:50] 2.142 0.244 -23.871 1.78 0.252 ...
## .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. ..$ : chr [1:22709] "HDM6_6WK_AAACCTGCAATTCCTT-1" "HDM6_6WK_AAACCTGCACATTTCT-1" "HDM6_6WK_AAACCTGCATCCGGGT-1" "HDM6_6WK_AAACCTGGTACAGTGG-1" ...
## .. .. .. .. .. ..$ : chr [1:50] "harmony_1" "harmony_2" "harmony_3" "harmony_4" ...
## .. .. .. ..@ feature.loadings : num [1:2000, 1:50] -397 -966 -509 -1003 8498 ...
## .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. ..$ : chr [1:2000] "Sox17" "St18" "Prex2" "Slco5a1" ...
## .. .. .. .. .. ..$ : chr [1:50] "harmony_1" "harmony_2" "harmony_3" "harmony_4" ...
## .. .. .. ..@ feature.loadings.projected: num [1:2000, 1:50] -397 -966 -509 -1003 8498 ...
## .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. ..$ : chr [1:2000] "Sox17" "St18" "Prex2" "Slco5a1" ...
## .. .. .. .. .. ..$ : chr [1:50] "harmony_1" "harmony_2" "harmony_3" "harmony_4" ...
## .. .. .. ..@ assay.used : chr "RNA"
## .. .. .. ..@ global : logi FALSE
## .. .. .. ..@ stdev : num [1:50] 5.53 4.36 4.18 4.6 3.25 ...
## .. .. .. ..@ jackstraw :Formal class 'JackStrawData' [package "SeuratObject"] with 4 slots
## .. .. .. .. .. ..@ empirical.p.values : num[0 , 0 ]
## .. .. .. .. .. ..@ fake.reduction.scores : num[0 , 0 ]
## .. .. .. .. .. ..@ empirical.p.values.full: num[0 , 0 ]
## .. .. .. .. .. ..@ overall.p.values : num[0 , 0 ]
## .. .. .. ..@ misc : list()
## .. .. .. ..@ key : chr "harmony_"
## .. ..$ umap :Formal class 'DimReduc' [package "SeuratObject"] with 9 slots
## .. .. .. ..@ cell.embeddings : num [1:22709, 1:2] 0.696 -4.174 12.588 -2.845 4.01 ...
## .. .. .. .. ..- attr(*, "scaled:center")= num [1:2] -0.247 -0.207
## .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. ..$ : chr [1:22709] "HDM6_6WK_AAACCTGCAATTCCTT-1" "HDM6_6WK_AAACCTGCACATTTCT-1" "HDM6_6WK_AAACCTGCATCCGGGT-1" "HDM6_6WK_AAACCTGGTACAGTGG-1" ...
## .. .. .. .. .. ..$ : chr [1:2] "umap_1" "umap_2"
## .. .. .. ..@ feature.loadings : num[0 , 0 ]
## .. .. .. ..@ feature.loadings.projected: num[0 , 0 ]
## .. .. .. ..@ assay.used : chr "RNA"
## .. .. .. ..@ global : logi TRUE
## .. .. .. ..@ stdev : num(0)
## .. .. .. ..@ jackstraw :Formal class 'JackStrawData' [package "SeuratObject"] with 4 slots
## .. .. .. .. .. ..@ empirical.p.values : num[0 , 0 ]
## .. .. .. .. .. ..@ fake.reduction.scores : num[0 , 0 ]
## .. .. .. .. .. ..@ empirical.p.values.full: num[0 , 0 ]
## .. .. .. .. .. ..@ overall.p.values : num[0 , 0 ]
## .. .. .. ..@ misc : list()
## .. .. .. ..@ key : chr "umap_"
## ..@ images : list()
## ..@ project.name: chr "HDMvsDA"
## ..@ misc : list()
## ..@ version :Classes 'package_version', 'numeric_version' hidden list of 1
## .. ..$ : int [1:3] 5 0 2
## ..@ commands :List of 8
## .. ..$ NormalizeData.RNA :Formal class 'SeuratCommand' [package "SeuratObject"] with 5 slots
## .. .. .. ..@ name : chr "NormalizeData.RNA"
## .. .. .. ..@ time.stamp : POSIXct[1:1], format: "2024-08-02 16:30:22"
## .. .. .. ..@ assay.used : chr "RNA"
## .. .. .. ..@ call.string: chr "NormalizeData(object = HDMDA_Obj_filtered)"
## .. .. .. ..@ params :List of 5
## .. .. .. .. ..$ assay : chr "RNA"
## .. .. .. .. ..$ normalization.method: chr "LogNormalize"
## .. .. .. .. ..$ scale.factor : num 10000
## .. .. .. .. ..$ margin : num 1
## .. .. .. .. ..$ verbose : logi TRUE
## .. ..$ FindVariableFeatures.RNA :Formal class 'SeuratCommand' [package "SeuratObject"] with 5 slots
## .. .. .. ..@ name : chr "FindVariableFeatures.RNA"
## .. .. .. ..@ time.stamp : POSIXct[1:1], format: "2024-08-02 16:30:23"
## .. .. .. ..@ assay.used : chr "RNA"
## .. .. .. ..@ call.string: chr "FindVariableFeatures(.)"
## .. .. .. ..@ params :List of 12
## .. .. .. .. ..$ assay : chr "RNA"
## .. .. .. .. ..$ selection.method : chr "vst"
## .. .. .. .. ..$ loess.span : num 0.3
## .. .. .. .. ..$ clip.max : chr "auto"
## .. .. .. .. ..$ mean.function :function (mat, display_progress)
## .. .. .. .. ..$ dispersion.function:function (mat, display_progress)
## .. .. .. .. ..$ num.bin : num 20
## .. .. .. .. ..$ binning.method : chr "equal_width"
## .. .. .. .. ..$ nfeatures : num 2000
## .. .. .. .. ..$ mean.cutoff : num [1:2] 0.1 8
## .. .. .. .. ..$ dispersion.cutoff : num [1:2] 1 Inf
## .. .. .. .. ..$ verbose : logi TRUE
## .. ..$ ScaleData.RNA :Formal class 'SeuratCommand' [package "SeuratObject"] with 5 slots
## .. .. .. ..@ name : chr "ScaleData.RNA"
## .. .. .. ..@ time.stamp : POSIXct[1:1], format: "2024-08-02 16:30:24"
## .. .. .. ..@ assay.used : chr "RNA"
## .. .. .. ..@ call.string: chr "ScaleData(.)"
## .. .. .. ..@ params :List of 9
## .. .. .. .. ..$ assay : chr "RNA"
## .. .. .. .. ..$ model.use : chr "linear"
## .. .. .. .. ..$ use.umi : logi FALSE
## .. .. .. .. ..$ do.scale : logi TRUE
## .. .. .. .. ..$ do.center : logi TRUE
## .. .. .. .. ..$ scale.max : num 10
## .. .. .. .. ..$ block.size : num 1000
## .. .. .. .. ..$ min.cells.to.block: num 3000
## .. .. .. .. ..$ verbose : logi TRUE
## .. ..$ RunPCA.RNA :Formal class 'SeuratCommand' [package "SeuratObject"] with 5 slots
## .. .. .. ..@ name : chr "RunPCA.RNA"
## .. .. .. ..@ time.stamp : POSIXct[1:1], format: "2024-08-02 16:30:35"
## .. .. .. ..@ assay.used : chr "RNA"
## .. .. .. ..@ call.string: chr "RunPCA(.)"
## .. .. .. ..@ params :List of 10
## .. .. .. .. ..$ assay : chr "RNA"
## .. .. .. .. ..$ npcs : num 50
## .. .. .. .. ..$ rev.pca : logi FALSE
## .. .. .. .. ..$ weight.by.var : logi TRUE
## .. .. .. .. ..$ verbose : logi TRUE
## .. .. .. .. ..$ ndims.print : int [1:5] 1 2 3 4 5
## .. .. .. .. ..$ nfeatures.print: num 30
## .. .. .. .. ..$ reduction.name : chr "pca"
## .. .. .. .. ..$ reduction.key : chr "PC_"
## .. .. .. .. ..$ seed.use : num 42
## .. ..$ Seurat..ProjectDim.RNA.harmony:Formal class 'SeuratCommand' [package "SeuratObject"] with 5 slots
## .. .. .. ..@ name : chr "Seurat::ProjectDim.RNA.harmony"
## .. .. .. ..@ time.stamp : POSIXct[1:1], format: "2024-08-02 16:30:45"
## .. .. .. ..@ assay.used : chr "RNA"
## .. .. .. ..@ call.string: chr [1:2] "Seurat::ProjectDim(object, reduction = reduction.save, overwrite = TRUE, " " verbose = FALSE)"
## .. .. .. ..@ params :List of 7
## .. .. .. .. ..$ reduction : chr "harmony"
## .. .. .. .. ..$ assay : chr "RNA"
## .. .. .. .. ..$ dims.print : int [1:5] 1 2 3 4 5
## .. .. .. .. ..$ nfeatures.print: num 20
## .. .. .. .. ..$ overwrite : logi TRUE
## .. .. .. .. ..$ do.center : logi FALSE
## .. .. .. .. ..$ verbose : logi FALSE
## .. ..$ FindNeighbors.RNA.harmony :Formal class 'SeuratCommand' [package "SeuratObject"] with 5 slots
## .. .. .. ..@ name : chr "FindNeighbors.RNA.harmony"
## .. .. .. ..@ time.stamp : POSIXct[1:1], format: "2024-08-02 16:30:48"
## .. .. .. ..@ assay.used : chr "RNA"
## .. .. .. ..@ call.string: chr "FindNeighbors(., reduction = \"harmony\", dims = 1:17)"
## .. .. .. ..@ params :List of 16
## .. .. .. .. ..$ reduction : chr "harmony"
## .. .. .. .. ..$ dims : int [1:17] 1 2 3 4 5 6 7 8 9 10 ...
## .. .. .. .. ..$ assay : chr "RNA"
## .. .. .. .. ..$ k.param : num 20
## .. .. .. .. ..$ return.neighbor: logi FALSE
## .. .. .. .. ..$ compute.SNN : logi TRUE
## .. .. .. .. ..$ prune.SNN : num 0.0667
## .. .. .. .. ..$ nn.method : chr "annoy"
## .. .. .. .. ..$ n.trees : num 50
## .. .. .. .. ..$ annoy.metric : chr "euclidean"
## .. .. .. .. ..$ nn.eps : num 0
## .. .. .. .. ..$ verbose : logi TRUE
## .. .. .. .. ..$ do.plot : logi FALSE
## .. .. .. .. ..$ graph.name : chr [1:2] "RNA_nn" "RNA_snn"
## .. .. .. .. ..$ l2.norm : logi FALSE
## .. .. .. .. ..$ cache.index : logi FALSE
## .. ..$ FindClusters :Formal class 'SeuratCommand' [package "SeuratObject"] with 5 slots
## .. .. .. ..@ name : chr "FindClusters"
## .. .. .. ..@ time.stamp : POSIXct[1:1], format: "2024-08-02 16:30:50"
## .. .. .. ..@ assay.used : chr "RNA"
## .. .. .. ..@ call.string: chr "FindClusters(., resolution = 0.5)"
## .. .. .. ..@ params :List of 11
## .. .. .. .. ..$ graph.name : chr "RNA_snn"
## .. .. .. .. ..$ cluster.name : chr "RNA_snn_res.0.5"
## .. .. .. .. ..$ modularity.fxn : num 1
## .. .. .. .. ..$ resolution : num 0.5
## .. .. .. .. ..$ method : chr "matrix"
## .. .. .. .. ..$ algorithm : num 1
## .. .. .. .. ..$ n.start : num 10
## .. .. .. .. ..$ n.iter : num 10
## .. .. .. .. ..$ random.seed : num 0
## .. .. .. .. ..$ group.singletons: logi TRUE
## .. .. .. .. ..$ verbose : logi TRUE
## .. ..$ RunUMAP.RNA.harmony :Formal class 'SeuratCommand' [package "SeuratObject"] with 5 slots
## .. .. .. ..@ name : chr "RunUMAP.RNA.harmony"
## .. .. .. ..@ time.stamp : POSIXct[1:1], format: "2024-08-02 16:31:03"
## .. .. .. ..@ assay.used : chr "RNA"
## .. .. .. ..@ call.string: chr "RunUMAP(., reduction = \"harmony\", dims = 1:17)"
## .. .. .. ..@ params :List of 25
## .. .. .. .. ..$ dims : int [1:17] 1 2 3 4 5 6 7 8 9 10 ...
## .. .. .. .. ..$ reduction : chr "harmony"
## .. .. .. .. ..$ assay : chr "RNA"
## .. .. .. .. ..$ slot : chr "data"
## .. .. .. .. ..$ umap.method : chr "uwot"
## .. .. .. .. ..$ return.model : logi FALSE
## .. .. .. .. ..$ n.neighbors : int 30
## .. .. .. .. ..$ n.components : int 2
## .. .. .. .. ..$ metric : chr "cosine"
## .. .. .. .. ..$ learning.rate : num 1
## .. .. .. .. ..$ min.dist : num 0.3
## .. .. .. .. ..$ spread : num 1
## .. .. .. .. ..$ set.op.mix.ratio : num 1
## .. .. .. .. ..$ local.connectivity : int 1
## .. .. .. .. ..$ repulsion.strength : num 1
## .. .. .. .. ..$ negative.sample.rate: int 5
## .. .. .. .. ..$ uwot.sgd : logi FALSE
## .. .. .. .. ..$ seed.use : int 42
## .. .. .. .. ..$ angular.rp.forest : logi FALSE
## .. .. .. .. ..$ densmap : logi FALSE
## .. .. .. .. ..$ dens.lambda : num 2
## .. .. .. .. ..$ dens.frac : num 0.3
## .. .. .. .. ..$ dens.var.shift : num 0.1
## .. .. .. .. ..$ verbose : logi TRUE
## .. .. .. .. ..$ reduction.name : chr "umap"
## ..@ tools : list()
head(HDMDA_Obj_Harm@meta.data)
## orig.ident nCount_RNA nFeature_RNA Id Time
## HDM6_6WK_AAACCTGCAATTCCTT-1 SeuratProject 1859 962 HDM6 6WK
## HDM6_6WK_AAACCTGCACATTTCT-1 SeuratProject 6871 2588 HDM6 6WK
## HDM6_6WK_AAACCTGCATCCGGGT-1 SeuratProject 3226 1227 HDM6 6WK
## HDM6_6WK_AAACCTGGTACAGTGG-1 SeuratProject 1637 842 HDM6 6WK
## HDM6_6WK_AAACCTGGTCAGATAA-1 SeuratProject 1919 1185 HDM6 6WK
## HDM6_6WK_AAACCTGGTCCAGTAT-1 SeuratProject 4150 1881 HDM6 6WK
## Barcode PercentMT RNA_snn_res.0.5
## HDM6_6WK_AAACCTGCAATTCCTT-1 AAACCTGCAATTCCTT-1 2.850995 5
## HDM6_6WK_AAACCTGCACATTTCT-1 AAACCTGCACATTTCT-1 1.673701 0
## HDM6_6WK_AAACCTGCATCCGGGT-1 AAACCTGCATCCGGGT-1 4.463732 10
## HDM6_6WK_AAACCTGGTACAGTGG-1 AAACCTGGTACAGTGG-1 3.543067 4
## HDM6_6WK_AAACCTGGTCAGATAA-1 AAACCTGGTCAGATAA-1 2.397082 5
## HDM6_6WK_AAACCTGGTCCAGTAT-1 AAACCTGGTCCAGTAT-1 2.024096 6
## seurat_clusters
## HDM6_6WK_AAACCTGCAATTCCTT-1 5
## HDM6_6WK_AAACCTGCACATTTCT-1 0
## HDM6_6WK_AAACCTGCATCCGGGT-1 10
## HDM6_6WK_AAACCTGGTACAGTGG-1 4
## HDM6_6WK_AAACCTGGTCAGATAA-1 5
## HDM6_6WK_AAACCTGGTCCAGTAT-1 6
# Top 20 variable genes
top_Vgenes <- head(VariableFeatures(HDMDA_Obj_Harm), 20)
plot1 <- VariableFeaturePlot(HDMDA_Obj_Harm)+ theme(legend.position = "none")
plot2 <- LabelPoints(plot = plot1, points = top_Vgenes, repel = TRUE)
## When using repel, set xnudge and ynudge to 0 for optimal results
plot1 + plot2
## Warning in scale_x_log10(): log-10 transformation introduced infinite values.
## log-10 transformation introduced infinite values.
## Warning: ggrepel: 7 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ElbowPlot(HDMDA_Obj_Harm)
PCHeatmap(HDMDA_Obj_Harm, dim = 1:6, cells = 500, balanced =TRUE, ncol = 3)
DimPlot(HDMDA_Obj_Harm, reduction = "umap", group.by = "RNA_snn_res.0.5", label = TRUE, label.box = TRUE)+ labs(title = "With Integration")
DimPlot(HDMDA_Obj_Harm, reduction = "umap", group.by = "RNA_snn_res.0.5", split.by = "Id", label = TRUE, label.box = TRUE)+ labs(title = "With Integration")
Annotate cell cluster
Cl_marker <- FindAllMarkers(HDMDA_Obj_Harm, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
## Calculating cluster 0
## For a (much!) faster implementation of the Wilcoxon Rank Sum Test,
## (default method for FindMarkers) please install the presto package
## --------------------------------------------
## install.packages('devtools')
## devtools::install_github('immunogenomics/presto')
## --------------------------------------------
## After installation of presto, Seurat will automatically use the more
## efficient implementation (no further action necessary).
## This message will be shown once per session
## Calculating cluster 1
## Calculating cluster 2
## Calculating cluster 3
## Calculating cluster 4
## Calculating cluster 5
## Calculating cluster 6
## Calculating cluster 7
## Calculating cluster 8
## Calculating cluster 9
## Calculating cluster 10
## Calculating cluster 11
## Calculating cluster 12
## Calculating cluster 13
## Calculating cluster 14
## Calculating cluster 15
head(Cl_marker)
## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## Il1rl1 0 3.461304 0.814 0.127 0 0 Il1rl1
## Fgl2 0 2.290296 0.851 0.310 0 0 Fgl2
## Gata3 0 2.058888 0.884 0.383 0 0 Gata3
## Rbpj 0 1.473229 0.829 0.370 0 0 Rbpj
## Il13 0 5.526502 0.458 0.021 0 0 Il13
## Ltb4r1 0 2.074159 0.540 0.150 0 0 Ltb4r1
dim(Cl_marker)
## [1] 8359 7
#View top markers for the specific cluster
Top10 <- Cl_marker %>% group_by(cluster) %>% top_n(n=10, wt = avg_log2FC)
DoHeatmap(HDMDA_Obj_Harm, features = Top10$gene) + NoLegend()
## Warning in DoHeatmap(HDMDA_Obj_Harm, features = Top10$gene): The following
## features were omitted as they were not found in the scale.data slot for the RNA
## assay: Eaf2, Derl3, Fkbp11, Nedd4, Ttk, Espl1, Ankrd12, AC149090.1, Macf1,
## Neat1, AU020206, mt-Atp8, mt-Cytb, mt-Co3, Malat1, Klhdc1, B630019A10Rik,
## Asap1, Gm47242, Lpar6, 1700020I14Rik, Trp53i13, Hnrnpll, 2010111I01Rik, Nebl
